[ https://issues.apache.org/jira/browse/HBASE-18164?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Kahlil Oppenheimer updated HBASE-18164:
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Status: Patch Available (was: Open)
> Much faster locality cost function and candidate generator
> ----------------------------------------------------------
>
> Key: HBASE-18164
> URL: https://issues.apache.org/jira/browse/HBASE-18164
> Project: HBase
> Issue Type: Improvement
> Components: Balancer
> Reporter: Kahlil Oppenheimer
> Assignee: Kahlil Oppenheimer
> Priority: Critical
> Fix For: 3.0.0, 1.4.0, 2.0.0-alpha-2
>
> Attachments: HBASE-18164-00.patch, HBASE-18164-01.patch, HBASE-18164-02.patch,
HBASE-18164-04.patch, HBASE-18164-05.patch, HBASE-18164-06.patch, HBASE-18164-07.patch, HBASE-18164-08.patch
>
>
> We noticed that during the stochastic load balancer was not scaling well with cluster
size. That is to say that on our smaller clusters (~17 tables, ~12 region servers, ~5k regions),
the balancer considers ~100,000 cluster configurations in 60s per balancer run, but only ~5,000
per 60s on our bigger clusters (~82 tables, ~160 region servers, ~13k regions) .
> Because of this, our bigger clusters are not able to converge on balance as quickly for
things like table skew, region load, etc. because the balancer does not have enough time to
"think".
> We have re-written the locality cost function to be incremental, meaning it only recomputes
cost based on the most recent region move proposed by the balancer, rather than recomputing
the cost across all regions/servers every iteration.
> Further, we also cache the locality of every region on every server at the beginning
of the balancer's execution for both the LocalityBasedCostFunction and the LocalityCandidateGenerator
to reference. This way, they need not collect all HDFS blocks of every region at each iteration
of the balancer.
> The changes have been running in all 6 of our production clusters and all 4 QA clusters
without issue. The speed improvements we noticed are massive. Our big clusters now consider
20x more cluster configurations.
> One design decision I made is to consider locality cost as the difference between the
best locality that is possible given the current cluster state, and the currently measured
locality. The old locality computation would measure the locality cost as the difference from
the current locality and 100% locality, but this new computation instead takes the difference
between the current locality for a given region and the best locality for that region in the
cluster.
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